Image Classification
Transformers
TensorBoard
Safetensors
vit
food-ingredient-classification
food101
food101-finetuned
Generated from Trainer
Instructions to use ericmconnelly/finetuned-food101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ericmconnelly/finetuned-food101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ericmconnelly/finetuned-food101") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ericmconnelly/finetuned-food101") model = AutoModelForImageClassification.from_pretrained("ericmconnelly/finetuned-food101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Eric Connelly commited on
🍻 cheers
Browse files- README.md +5 -1
- all_results.json +8 -0
- train_results.json +8 -0
- trainer_state.json +0 -0
README.md
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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# finetuned-food101
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on
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It achieves the following results on the evaluation set:
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- Loss: 0.6105
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- Accuracy: 0.8400
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- image-classification
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- food-ingredient-classification
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- food101
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- food101-finetuned
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- generated_from_trainer
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metrics:
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- accuracy
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# finetuned-food101
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6105
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- Accuracy: 0.8400
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all_results.json
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{
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"epoch": 4.0,
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"total_flos": 1.9975615705569485e+19,
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"train_loss": 0.9840855360623473,
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"train_runtime": 18351.2913,
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"train_samples_per_second": 14.034,
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"train_steps_per_second": 0.877
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}
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train_results.json
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{
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"epoch": 4.0,
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"total_flos": 1.9975615705569485e+19,
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"train_loss": 0.9840855360623473,
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"train_runtime": 18351.2913,
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"train_samples_per_second": 14.034,
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"train_steps_per_second": 0.877
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}
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trainer_state.json
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